A Short-Term Wind Power Forecasting Approach With Adjustment of Numerical Weather Prediction Input by Data Mining

Xu, Q.; He, Dawei; Zhang, Ning; Chen, Kang; Xia, Qing; Bai, Jing; Huang, Junhui

doi:10.1109/tste.2015.2429586

Cited by 168 publications

(71 citation statements)

References 31 publications

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“…(3) The conventional features in the previous supervised forecasting models contain the NWP data of the wind farms [15][16][17][18][19]. However, the NWP model usually runs once or twice a day, which is often applied for medium-to long-term forecasts [20].…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Multistep Wind Speed and Wind Power Prediction Based on a Predictive Deep Belief Network and an Optimized Random Forest

Sun

Zhang

2018

Mathematical Problems in Engineering

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A variety of supervised learning methods using numerical weather prediction (NWP) data have been exploited for short-term wind power forecasting (WPF). However, the NWP data may not be available enough due to its uncertainties on initial atmospheric conditions. Thus, this study proposes a novel hybrid intelligent method to improve existing forecasting models such as random forest (RF) and artificial neural networks, for higher accuracy. First, the proposed method develops the predictive deep belief network (DBN) to perform short-term wind speed prediction (WSP). Then, the WSP data are transformed into supplementary input features in the prediction process of WPF. Second, owing to its ensemble learning and parallelization, the random forest is used as supervised forecasting model. In addition, a data driven dimension reduction procedure and a weighted voting method are utilized to optimize the random forest algorithm in the training process and the prediction process, respectively. The increasing number of training samples would cause the overfitting problem. Therefore, the k-fold cross validation (CV) technique is adopted to address this issue. Numerical experiments are performed at 15-min, 30-min, 45-min, and 24-h to indicate the superiority and signal advantages compared with existing methods in terms of forecasting accuracy and scalability.

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Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Multistep Wind Speed and Wind Power Prediction Based on a Predictive Deep Belief Network and an Optimized Random Forest

Sun

Zhang

2018

Mathematical Problems in Engineering

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“…The power generation forecasting of small hydropower stations from June to October in 2015 were predicted and corrected using Formula (3) and (6). The predicted errors of maximum load before correction are shown in Table II.…”

Section: Power Generation Forecasting Of Small Hydropower Stationsmentioning

confidence: 99%

“…A great deal of research work on the influence of meteorological factors on the load forecasting curve has been conducted by scholars around the world [1][2][3][4][5][6], but there are still several aspects that should be considered.…”

Section: Introductionmentioning

confidence: 99%

The Regional Small Hydropower Generated Energy Forecasting Method

Han¹,

Qin²,

Lin³

et al. 2017

Proceedings of the 2016 International Conference on Engineering and Advanced Technology

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To improve forecasting accuracy of the generated energy of regional small hydropower stations, it is proposed a forecasting method of regional small hydropower generation considering the cumulative effect of rainfall.In this paper, the meteorological data was classified by using decision tree firstly. Then through the in-depth analysis of the output law of small hydropower stations, the generated energy forecasting model was set up using the method of multiple regression fitting that considering the rainfall effect. Finally, the corrected model is established to correct the predicted results that considering accumulative effect of rainfall. INTRODUCTIONAs an important clean renewable energy, small hydroelectric power plants have following features. The installed capacity is small, the construction period is short, the output is closely related to weather, and they are widespread throughout the area with abundant water resources.With the installment capacity of small hydroelectric power plants growing, its proportion in total electrical generating capacity is rising. Due to the small reservoir capacity and no energy storage, rainfall greatly influences the generating capacity of small hydroelectric power plants, that is, the generating capacity of small hydroelectric power plants depends on the rainfall and then the wholesale electricity is affected indirectly.A great deal of research work on the influence of meteorological factors on the load forecasting curve has been conducted by scholars around the world[1-6], but there are still several aspects that should be considered.(1) The complexity and variability of weather ,including the weather changes continuously in time,and the weather might abruptly change at the same time.(2) Dual nature of the influence of weather on load. One hand, the effect of real-time weather changes on the load. On the other hand, the effect of meteorological cumulative effect on the load.For the sake of those problems mentioned above, a forecasting method based on meteorological decision tree was proposed ,and the monthly generated energy of small hydropower stations was predicted by using the method in this paper.

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“…The physical approach can establish a specialized wind power forecasting model for a wind farm without large amount of historical data. In particular, this approach needs detailed physical characteristics of wind turbines and wind farm to achieve an accurate model [5]. However, it is difficult to collect these physical characteristics in a short period of time.…”

Section: Introductionmentioning

confidence: 99%

A Data Mining Approach Combining $K$ -Means Clustering With Bagging Neural Network for Short-Term Wind Power Forecasting

Peng

2017

IEEE Internet Things J.

131

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Wind power forecasting (WPF) is significant to guide the dispatching of grid and the production planning of wind farm effectively. The intermittency and volatility of wind leading to the diversity of the training samples have a major impact on the forecasting accuracy. In this paper, to deal with the training samples dynamics and improve the forecasting accuracy, a data mining approach consisting of K-means clustering and bagging neural network is proposed for short-term WPF. Based on the similarity among historical days, K-means clustering is used to classify the samples into several categories, which contain the information of meteorological conditions and historical power data. In order to overcome the over fitting and instability problems of conventional networks, a bagging-based ensemble approach is integrated into the back propagation neural network. To confirm the effectiveness, the proposed data mining approach is examined on real wind generation data traces. The simulation results show that it can obtain better forecasting accuracy than other baseline and existed short-term WPF approaches.

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A Short-Term Wind Power Forecasting Approach With Adjustment of Numerical Weather Prediction Input by Data Mining

Cited by 168 publications

References 31 publications

Multistep Wind Speed and Wind Power Prediction Based on a Predictive Deep Belief Network and an Optimized Random Forest

Multistep Wind Speed and Wind Power Prediction Based on a Predictive Deep Belief Network and an Optimized Random Forest

The Regional Small Hydropower Generated Energy Forecasting Method

A Data Mining Approach Combining $K$ -Means Clustering With Bagging Neural Network for Short-Term Wind Power Forecasting

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